A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC)

J. M. Zobitz, A. R. Desai, D. J.P. Moore, M. A. Chadwick

Research output: Contribution to journalReview articlepeer-review

69 Scopus citations


Data assimilation, or the fusion of a mathematical model with ecological data, is rapidly expanding knowledge of ecological systems across multiple spatial and temporal scales. As the amount of ecological data available to a broader audience increases, quantitative proficiency with data assimilation tools and techniques will be an essential skill for ecological analysis in this data-rich era. We provide a data assimilation primer for the novice user by (1) reviewing data assimilation terminology and methodology, (2) showcasing a variety of data assimilation studies across the ecological, environmental, and atmospheric sciences with the aim of gaining an understanding of potential applications of data assimilation, and (3) applying data assimilation in specific ecological examples to determine the components of net ecosystem carbon uptake in a forest and also the population dynamics of the mayfly (Hexagenia limbata, Serville). The review and examples are then used to provide guiding principles to newly proficient data assimilation practitioners.

Original languageEnglish (US)
Pages (from-to)599-611
Number of pages13
Issue number3
StatePublished - Nov 2011


  • Aquatic insects
  • Data assimilation
  • Ecological models
  • Markov Chain Monte Carlo
  • NEE

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics


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